Technological advances in computer hardware, software, and networking have lead to efficient, cost effective computing systems (e.g., desktop computers, laptops, handhelds, cell phones, servers . . . ) that can communicate with each other from essentially anywhere in the world. Such systems continue to evolve into more reliable, robust and user-friendly systems. As a consequence, more and more industries and consumers are purchasing computers and utilizing them as viable electronic alternatives to traditional paper and verbal media for exchanging information. Many industries and consumers are leveraging computing technology to improve efficiency and decrease cost. For instance, consumers can scan and store documents, create an album of digital images with text overlays, search and retrieve specific information (e.g., web pages with various types of data), upload pictures from digital cameras, view financial statements, transmit and/or receive digital facsimiles, exchange correspondence (e.g., email, chat rooms, voice over IP . . . ), etc.
As a result, such computing systems and/or devices have incorporated a variety of techniques and/or methods for inputting information. Computing systems and/or devices facilitate entering information utilizing devices such as, but not limited to, keyboards, keypads, touch pads, touch-screens, speakers, stylus' (e.g., wands), writing pads, . . . However, input devices that leverage user handwriting bring forth user personalization deficiencies in which each user can not utilize the data entry technique (e.g., writing) similarly.
A user's handwriting can be as unique as the user, wherein such uniqueness can be used for identification purposes. Commercial handwriting recognition systems implemented within various computing systems and/or devices attempt to reduce the impact of writer variation through utilizing large training datasets including data from a plurality of disparate users. Even when handwriting samples from as many as 1500 users are available, there is sufficient variation in the handwriting to uniquely identify each of the users.
From a machine learning perspective, such variation makes handwriting recognition difficult for computers. While intra-user characters (e.g., from the same user) have small variations, inter-user characters (e.g., from different users) have large variations and contribute to recognition errors. As a result, learning from training data obtained from one set of users (even hundreds of users) does not necessarily produce models that generalize well to unseen handwriting styles. The computer recognition experience using a generic (e.g., writer-independent) recognizer can be especially poor for users with rare writing styles. One explanation for the poor performance can be that the trained generic recognizer is incomplete as it has not learned to recognize unseen user's writing style(s).
A pragmatic approach to improving recognizer performance on unseen writing styles is writer adaptation (or personalization). Personalization enables the recognizer to adapt to a particular user's handwriting by collecting and learning from additional data samples from the user. Clearly, there is a trade off between the number of training samples needed from the user, the achieved reduction in error rate, and the perceived inconvenience to the user. The larger the amount of training data, the better the personalized recognizer, but the more inconvenience for the user based on input of samples, and/or training utilizing such samples.